Automated cavity filter tuning using machine learning

ABSTRACT

A test and measurement instrument includes one or more ports to connect to one or more devices under test (DUT) having tuning screws, and to a robot, one or more processors to configured to: send commands to the robot to position the tuning screws on the one or more DUTs to one or more sets of positions, each set of positions being a parameter set for the tuning screws, acquire a set of operating parameters for each parameter set from the one or more DUTs, generate a parameter set image for each set, create a combined image of the parameter set images, provide the combined image to a machine learning system to obtain a predicted set of values, adjust the predicted set of values to produce a set of predicted positions, send commands to the robot to position the tuning screws to positions in the set of predicted positions, obtain a set of tuned operating parameters from the one or more DUTs, and validate operation of the one or more DUTs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure claims benefit of U.S. Provisional Application No.63/351,291, titled “AUTOMATED CAVITY FILTER TUNING USING MACHINELEARNING,” filed on Jun. 10, 2022, the disclosure of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

This disclosure relates to test and measurement systems, and moreparticularly to instruments, methods, and systems that employ machinelearning for automated tuning of a cavity filter or other similar deviceunder test (DUT).

BACKGROUND

RF cavity filters, diplexers, and duplexers are common RF components intransmitter, receiver, and transceiver circuits for cellular andsatellite communications, radar, and other sensing applications. Thesedevices are all tunable, typically using tuning screws.

As an example, a diplexer is a 3-port RF device, which enables the useof two signal paths on the same antenna or transmission line. This isdone by frequency division using filters, either high-pass, low-pass, orband-pass filters. The same antenna could send and receive signals attwo different frequencies. For a diplexer to function well, the qualityand attenuation of the filters must scale with how close the signals arein frequency, at what power levels they operate, and what nonlinearitiesare expected.

Diplexers are often used in telecommunications, where many modulationmethods and carriers operate on the same antenna. For example, cellularbase stations can transmit and receive CDMA (Code-Division MultipleAccess), LTE (Long-Term Evolution), or GSM (Global System for Mobilecommunications) signals on the same antenna, as a cell site.

A duplexer provides another example, also a 3-port RF device. Itseparates transmit and receive signals from an antenna to two differentsignal paths based on direction. These transmit and receive signals mayoperate at the same frequency. A duplexer enables true two-waycommunication from a single antenna. For example, a duplexer can be usedin a radar system where the high-power transmitter signals need to beisolated from the sensitive receiver circuitry but operate on the sameantenna.

Either switched systems or magnetic circulators are used to create theisolation between the incoming and outgoing signals within a duplexer.Duplexers are limited by how well they can isolate the receive path fromthe transmit path. With radar transmit/receive (TR) modules, thetransmit and receive frequencies are typically remarkably close, and canonly reasonably be separated through duplexing.

FIG. 1 shows an example of a duplexer and its many tuning screws. Thethree ports of a duplexer comprise one input and two outputs. Eachoutput has a transfer function of a bandpass filter with differentcenter frequencies adjacent to each other as shown in FIG. 2 . Somedevices may incorporate a low pass filter and high pass filter. FIG. 3shows a circuit simulation model of two of the three ports from input toone of the bandpass outputs.

As mentioned above, one can tune the characteristics of these devicesusing tuning screws. Traditionally, manufacturers tune these tuningscrews manually, which takes a long time in manufacturing, raising costsand delaying manufacturing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a duplexer having tuning screws.

FIG. 2 shows an example plot of the two bandpass responses of aduplexer.

FIG. 3 shows an example of a circuit diagram for half of a duplexer.

FIG. 4 shows a set of tuning screws set to a zero-reference position.

FIG. 5 shows a set of tuning screws set to the max reference position.

FIG. 6 shows a set of tuning screws set to the average optimally tunedposition.

FIG. 7 shows a block diagram of an embodiment of training a machinelearning system for robot-controlled tuning of cavity filters.

FIG. 8 shows a block diagram of an embodiment of using a machinelearning system for runtime robot-controlled tuning of cavity filters.

FIGS. 9-11 show Smith chart views of S-parameters of a duplexer.

FIGS. 12-14 show magnitude versus frequency plots for S-parameters of aduplexer.

FIGS. 15-16 show examples of RGB tensor images.

FIG. 17 shows an alternative example of tensor images.

FIG. 18 shows a diagram of integrating four sets of three referenceparameters for training a machine learning system.

FIG. 19 shows a block diagram of an embodiment of training a machinelearning system for robot-controlled tuning of cavity filters using foursets of three position sets.

FIG. 20 shows a block diagram of an embodiment of using a machinelearning system for runtime robot-controlled tuning of cavity filtersusing four sets of three position sets.

DESCRIPTION

The embodiments perform robot tuning of cavity filters, such asdiplexers, and duplexers and/or other similar kinds of cavity-tuningdevices which have tuning screws. Wireless communications antennainstallations use these types of devices extensively. The embodimentsuse a one-time prediction from a trained deep learning neural network. Arobot then positions the tuning screws to the predicted optimal tuningposition. Traditionally, tuning the devices manually takes a very longtime in manufacturing. The machine-learning-based process according toembodiments would greatly reduce the amount of time to tune each device.

U.S. patent application Ser. No. 17/701,186, titled “OPTICAL TRANSMITTERTUNING USING MACHINE LEARNING AND REFERENCE PARAMETERS,” filed Mar. 22,2022, the entire contents of which are hereby incorporated by reference,describes systems and methods for performing tuning of opticaltransceivers using multiple sets of reference tuning parameters.Embodiments apply the concept of sets of reference tuning parameters tothe tuning of cavity filters and similar DUTs. For example, in the caseof a duplexer, a position vector represents the tuning parameters as theposition vector for a set of tuning screws. The tuning screw positionmay be represented as an angle of rotation from a reference position, orit may be represented as linear depth position of the screw insertedinto the tuning cavity.

The process creates a set of multiple reference tuning parameter sets orvectors to characterize the device for the neural network, referred toherein as parameter sets. Some embodiments use three parameter sets. Thediscussion below will focus on three parameter sets to provide ease ofunderstanding, but more or fewer reference parameter sets may also beused. In the case of three reference parameter sets the three referenceparameter sets may be referred to as delta1Par, delta2Par, and meanPar.The delta1Par reference set has set all the tuning screws to theirmaximum depth into the tuning cavity. This can be defined to be thedelta1Par reference parameter set, or the maximum parameter set, shownin FIG. 4 .

In the delta2Par reference set, or the minimum parameter set, the robothas set all the tuning screws to their minimum depth into the tuningcavity, shown in FIG. 5 .

For the mean reference parameter set, meanPar, shown in FIG. 6 , therobot has set all the tuning screws to their average depth into thetuning cavity for an average set of duplexers that have been optimallytuned.

One should note that the three reference parameter sets do not have tocomprise the same three position sets described above. Other sets ofpositions may be chosen depending on how well they perform forcharacterization of the cavity filters to the deep learning network.However, the meanPar set has particular interest because it provides theaverage of all the optimally tuned devices for large numbers of devices,but it can also be a different setting if desired. Further, while thediscussion here uses three sets, and more or fewer sets may be used,more than likely, the process will achieve better results with at leastthree sets.

FIG. 7 shows an embodiment of a system and method to train the deeplearning network to associate a DUT set of S-parameters in order topredict an optimal tuning set of screw positions for the duplexer,diplexer, cavity filter, or similar cavity-tunable DUT. The example hereuses a duplexer. S-parameters, or scattering parameters, describe theinput-output relationships between ports in an electrical system. S11 isthe input port voltage reflection coefficient, S12 is the reversevoltage gain, S21 is the forward voltage gain, and S22 is the outputport voltage reflection coefficient, S32 is the far-end cross talk. Oneshould note that the use of S-parameters provides one example ofoperational parameters. Other examples include S-parameters in the timedomain, Z-parameters (open circuit or impedance parameters),H-parameters (hybrid parameters), X-parameters (generalization ofS-parameters), and many others.

One should note that the embodiments do not need to use all theS-parameters in the 3-port set as input for training the deep learningnetwork. The duplexer is a passive device where physics dictates,S31=S13, and S21=S12. In addition, the cross-coupling terms of S32=S23.Therefore, when the tensor images are created, only S21, S11, S22, S32,and S33 may be incorporated into the tensor image for training,discussed below. An XY real vs imaginary (RI) chart representation ofeach parameter is shown in FIG. 9 , FIG. 10 , and FIG. 11 . Other typesof plots could be used, such as magnitude and phase (MP), which may bepreferred for insertion and crosstalk terms, and impedance (Z) plots vsreflection coefficient (S11) plots. The S32 Smith chart is not shown butwould be used in this procedure.

FIG. 7 shows a test and measurement instrument that can implement aprocedure for training a machine learning system 18 to be used forduplexer tuning, as an embodiment of a method to tune tunable cavityDUTs. Instrument 10 has ports 12 and 14, port 12 to allow the instrumentto communicate with a deep learning neural network, and port 14 tocommunicate with a robot that can receive commands from the instrumentto tune the cavity on the device. Port 16 receives the S-parameters fromthe VNA and sends the S-parameter RGB image through port 12 to themachine learning system.

The training process uses a large number of DUTs for training purposes,and uses the instrument and the robot, and the user's conventionalmanual or non-manual tuning procedure, to tune each device and recordthe position of the screws for the optimal tuning. The screw positionsfor the optimal tuning make up the optimal tuning parameter set for eachindividual duplexer. The instrument may comprise a Vector NetworkAnalyzer (VNA), such as the Tektronix TTR506A Vector Network Analyzer.This particular example has three-port VNA which may measure S21, S11,S22, S32, and S33 on the three port filter networks. The instrument mayuse some external switches to make the appropriate connections for eachmeasurement.

Once the instrument has the optimal parameter set for each DUT, one ormore processors in the instrument may execute code to cause the one ormore processors to perform the methods of the embodiments. In thisinstance, the one or more processors would compute the mean for eachparameter, meaning each tuning screw position, of all the optimal tuningsets to obtain the meanPar reference parameter set. While theembodiments here show the machine learning system 18 to be separate fromthe instrument for ease of discussion, no such limitation exists. Theinstrument may include any or all the machine learning system, thenormalization and denormalization blocks, and the position controllerthat controls the robot.

In one embodiment the process then defines three sets of referenceposition parameters. This discussion will refer to these as delta1Par,delta2Par, and meanPar, as discussed above. The instrument then sendscommands to the robot to position the tuning screws to the delta1Parposition and acquires a first set of 3-port S-parameters for the DUTs.The instrument then sends commands to the robot to position the screwsto the delta2Par reference position and acquires a second set of 3-portS-parameters for the DUT. In this embodiment, the instrument then sendsone last set of commands to the robot to position the screws to themeanPar reference position and acquires a third set of 3-portS-parameters for the duplexer.

Once the instrument has the three sets of reference S-parameters, itgenerates a parameter image of an S-parameter plot. In one embodiment,as discussed above, this comprises the XY real vs imaginary plot for theS-parameters. Imaginary values of the S-parameters are on the verticalaxis, and the real part of the S-parameters are on the X-axis. FIGS.9-11 show Smith chart plots for the S11, S22, and S21 parameters,discussed in more detail later. One embodiment uses four of theS-parameters listed above, but the system could use more or fewerS-parameters.

Generation of a combined image, in one embodiment of a tensor image,involves placing one XY plot of each of the S-parameters into onequadrant of the image. Using four S-parameters results in each quadranthaving an image. In other embodiments, these plots could be arranged indifferent ways. Each parameter set ends up with its own image, with eachparameter extracted from the parameter set in a different quadrant ofthe image. These images then combine to form a combined image. In oneembodiment, each parameter set image is placed onto a different colorchannel of an RGB image. In this embodiment, the parameter image fordelta1Par into the red channel, the parameter image for delta2Par intothe blue channel, and the meanPar image into the green channel. Theprocess then creates an array of such RGB images, with one RGB image foreach duplexer.

Prior to training, the data is normalized. In one embodiment, thisinvolves subtracting the meanPar data set from the optimal tuningparameter data set for each transmitter. The process then uses min/maxnormalization to scale the metadata sets to be with a range of 0.9 to−0.9. This places the data into a range of neural net layers that havecompression as the data approaches one or −1. The results are thenplaced into an array. The index of metadata optimal tuning set shallcorrespond to the index in the array of position vectors for thecorresponding three sets of RGB S-parameters representing thecharacterization of the transmitter for that optimal tuning.

The process then uses the array of metadata and the array ofS-parameters to train the machine learning system.

After the deep learning network has been trained, it is then ready touse on the manufacturing line to receive reference position S-parametersfrom the device to tune, and then make a prediction on what the screwpositions shall be for optimal tuning for that transmitter. The robotthen rotates the screws to the optimal predicted tuning position. FIG. 8shows an embodiment of this process.

The process begins by connecting the DUT to be tuned to the test andmeasurement instrument, such as a 3-port VNA. The instrument then sendscommands to position the screws to one or more sets of positions,wherein each set of positions make up a parameter set for the tuningscrews. As in the above discussion, this discussion will also use threeparameter sets.

The instrument sends commands to the robot to position the screws todelta1Par position and then acquires the operational parameters, e.g.,S-parameters, for that set. The instrument then sends commands to therobot to position the screws to the meanPar positions and acquires theoperational parameters for that set of parameters. Finally, theinstrument will send the commands to the robot to position the screws todelta2Par position and acquires the operational parameter set for thosesettings.

Once the operational parameter sets are acquired, the instrument canmake up the RGB image using the same procedure as in training, bygenerating parameter images and then combining them into one image.These are then placed onto the channels of an RGB image by combining theparameter images for the delta1Par S-parameters used into the quadrantsof one image and placing them on the red channel, combining theparameter images for the meanPar S-parameters into the quadrants of oneimage and placing it in the green channel of the RGB image, thencombining the delta2Par S-parameters into quadrants of one image andplacing it in the blue channel of the RGB image.

The instrument then sends the RGB image to the trained machine learningsystem to obtain a set of predicted values. As discussed previously, thedata provided to the machine learning system may be normalized, so theresults need to be de-normalized. The denormalized data is then added tothe meanPar tuning parameter set. This should set the DUT to its properoperating settings to tune the DUT. The instrument then sends commandsto the robot to set the tuning screws to the positions predicted by theresult of the previous step. The operation of the DUT is then validated.

FIGS. 9-11 show Smith chart plots for S11, S22, and S21 of the simulatedhalf of a duplexer circuit as shown in FIG. 3 . The Smith chart iscreated by simply plotting an S-parameter set of real and imaginaryvalues on an XY axis and then drawing the Smith chart grid for it. Theimage tensors for input to the deep learning network do not include theSmith chart grids, but only the XY plot of the S-parameter data. Oneshould note that in practice a fourth plot for S32 would also beincorporated. In addition, the process may incorporate S33 and S31.

FIGS. 12-14 show log plots for magnitude vs frequency. According to someembodiments of the disclosure, it is also possible to use these plots tocreate the tensor images to use for input to the deep learning network,rather than the Smith chart plots. For this approach, the phase plot ofeach S-parameter would also be incorporated and overlaid with themagnitude vs frequency plot.

FIG. 15 shows an example of the RGB image that would be input to thedeep learning network for the purpose of training the network and forthe purpose of making a tuning screw position prediction. In oneembodiment, the red channel 20 contains the four S-parameters, S11, S22,S21, and S31 that were measured when the robot had set the tuning screwsto the delta1Par reference parameter position. The green channel 22contains the S-parameters, S11, S22, S21, and S31 that were measuredwhen the robot positions the tuning screws to the meanPar referenceposition. The blue channel 24 contains the S-parameters S11, S22, S21,and S31 when the tuning screws were positioned to the delta2Parposition. Other embodiments may use different ordering of theS-parameters for each of the reference parameter sets into whichever ofthe red, green, and blue channels in constructing the tensor image.Again, one should note that these examples do not include S32 with theassumption it is a small value, and that it is not needed to predict thedesired tuning screw positions, but it could be included as well.

Each of the four S-parameters appears in one of the four quadrants ofthe image. Showing RI, real and imaginary, on all four S-parameters inFIG. 15 leaves the Z-axis which represents frequency coming straight outof the plots. The frequency on the Z-axis is represented as intensity inthe image.

However, an alternative tensor image construction would be to plot S21as log magnitude vs frequency plot while leaving the reflectioncoefficients S11, and S22 and S33 as RI plots to show impedance. FIG. 16shows an example of this tensor image construction. These ports need tobe matched to the impedance of the antenna or transmitter to which theyare connected.

FIG. 17 shows another example of a combined image, also a tensor image,but in which the frequency lies on the image X-axis, amplitude on theZ-axis, and each real and imaginary S-parameter component appearing onthe Y-axis. Each of these parameter set images is then placed in adifferent color channel of the combined RGB image to be analyzed by thedeep learning network.

The deep learning network may consist of any type of layered model thatcan be made to function with the accuracy desired in this system. Oneexample implementation, according to some embodiments of the disclosure,uses a pretrained deep learning network and reconfigure it to usetransfer learning. For example, a Resnet18 network which is pre trainedto recognize 244×244 pixel RGB images of cats, dogs, keyboards, etc. maybe used. The three output layers called fully connected, RELU, andclassification are removed and replaced with an untrained fullyconnected layer, and an output regression layer.

In this example implementation, the many layers of the network are notretrained. Only the two new layers are trained by providing an array ofS-parameter tensor images, and an array of metadata vectors representingthe optimally tuned positions of the screws for each associated RGBS-parameter image.

Alternative embodiments of the disclosure may convert the S-parameterdata to the time domain and use the resulting images as input to thethree RGB channels of the constructed tensor images for training andprediction. Then time domain instruments, such as an oscilloscopecoupled to a step generator and appropriate fixture routing of signalsto the device to be measured, may directly measure the representation ofthe S-parameters, rather than the VNA shown in FIGS. 7-8 .

According to another aspect of the embodiments, for similar precisionelectromechanical filter devices, the process may use fewer units toproduce the necessary training data by collecting data at variousreference parameter settings, meaning the tuning screw positions.Instead of using just one triplet set of reference parameters, many setsof triplet reference parameters on the same device may increase thenumber of data input samples for training for a given number of devices.FIG. 18 illustrates the use of multiple sets of triplet referenceparameters.

FIG. 18 shows four sets of data in the diagram, with the center or meanof each set denoted as P1, P2, P3, and P4. Each set has three screwlocations as discussed above, such as high, center, and low. The robotmoves the screws to various locations to get multiple sets of data. Thedistance to the optimal location, for example the difference between thecenter of each triplet data set and the optimal location, makes thelabel for each of the data sets. FIG. 18 denotes these differences asL1, L2, L3, and L4. These differences provide the metadata input usedfor training the network. During runtime, the network predicts thisdata.

This technique allows for the use of fewer filter units to get largedata sets for training. However, a need still exists to use enoughsamples of filter units to let the machine learning model observe thevariation between units. For the same number of units used for training,the system obtains more data for machine learning model training, whichimproves the accuracy of the model.

The metadata into the deep learning network would be defined as follows:

L=P0−Pn2

where P0 is the optimally tuned screw position, and where Pn2 is themiddle set of screw positions out of the three reference parameter setsused. Then L is the set of differences that are input to the deeplearning network when it is trained. FIG. 19 shows a training systemusing four sets of three position sets for training the deep learningnetwork.

Then during run time, the three reference screw position parameter setsare used to measure the filter device S-parameters and make an RGB imageto input to the deep learning network which makes a prediction for Lafter the de-normalization block, as shown in FIG. 20 . Then the actualscrew positions are computed as:

P0=L+Pn2

The system shown in FIG. 20 represents the trained system in runtime forprediction of optimal screw positions to optimally tune the filter.

To make a prediction, the process chooses one of the four possibletriplet sets of positions. The process then feeds the screw position foreach of the reference position triplet set to the robot to set thepositions. The test and measurement device then measures the 3-portS-parameter set for each of the three reference sets. The process thenmakes the three images as described for training and placed in each ofthe RGB channels. The deep learning network then receives the RGB image,and it provides the prediction for screw position for the optimal tuningof the filter.

Embodiments of the disclosure provide a novel system for tuning the setscrews for diplexers and duplexers and other devices with tuning screws,using reference parameter settings and machine learning to predict theoptimal settings for the tuning screws. This is accomplished bycollecting a set of reference parameters for many cavity filter devicesamples that are set to three reference parameter screw positionsettings. Then training the neural network to associate those threereference-parameter sets from the device when it is tuned to the optimalscrew position settings to obtain the calibrated desired S-parameters.Thus, the trained deep learning network observes a tensor imagecontaining the S-parameters of the device from the three referenceposition screw settings, and then predicting what the optimal tunedscrew positions are. In addition, a novel approach is taken to increasethe number of reference parameter triplet sets to any number desired.This allows for a much larger number of training data inputs for a givennumber of devices. This latter approach will work well for devices thatare precision and do not show much variation from device to device.Embodiments may be used to automatically tune any device having tuningelements in which the physical position of the tuning elements affectsthe measured S-parameters of the device.

Aspects of the disclosure may operate on a particularly createdhardware, on firmware, digital signal processors, or on a speciallyprogrammed general-purpose computer including a processor operatingaccording to programmed instructions. The terms controller or processoras used herein are intended to include microprocessors, microcomputers,Application Specific Integrated Circuits (ASICs), and dedicated hardwarecontrollers. One or more aspects of the disclosure may be embodied incomputer-usable data and computer-executable instructions, such as inone or more program modules, executed by one or more computers(including monitoring modules), or other devices. Program modulesinclude routines, programs, objects, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes when executed by a processor in a computer or other device. Thecomputer executable instructions may be stored on a non-transitorycomputer readable medium such as a hard disk, optical disk, removablestorage media, solid state memory, Random Access Memory (RAM), etc. Aswill be appreciated by one of skill in the art, the functionality of theprogram modules may be combined or distributed as desired in variousaspects. In addition, the functionality may be embodied in whole or inpart in firmware or hardware equivalents such as integrated circuits,FPGA, and the like. Particular data structures may be used to moreeffectively implement one or more aspects of the disclosure, and suchdata structures are contemplated within the scope of computer executableinstructions and computer-usable data described herein.

The disclosed aspects may be implemented, in some cases, in hardware,firmware, software, or any combination thereof. The disclosed aspectsmay also be implemented as instructions carried by or stored on one ormore or non-transitory computer-readable media, which may be read andexecuted by one or more processors. Such instructions may be referred toas a computer program product. Computer-readable media, as discussedherein, means any media that can be accessed by a computing device. Byway of example, and not limitation, computer-readable media may comprisecomputer storage media and communication media.

Computer storage media means any medium that can be used to storecomputer-readable information. By way of example, and not limitation,computer storage media may include RAM, ROM, Electrically ErasableProgrammable Read-Only Memory (EEPROM), flash memory or other memorytechnology, Compact Disc Read Only Memory (CD-ROM), Digital Video Disc(DVD), or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, and any othervolatile or nonvolatile, removable or non-removable media implemented inany technology. Computer storage media excludes signals per se andtransitory forms of signal transmission.

Communication media means any media that can be used for thecommunication of computer-readable information. By way of example, andnot limitation, communication media may include coaxial cables,fiber-optic cables, air, or any other media suitable for thecommunication of electrical, optical, Radio Frequency (RF), infrared,acoustic or other types of signals.

Additionally, this written description refers to particular features. Itis to be understood that the disclosure in this specification includesall possible combinations of those particular features. For example,where a particular feature is disclosed in the context of a particularaspect, that feature can also be used, to the extent possible, in thecontext of other aspects.

The previously described versions of the disclosed subject matter havemany advantages that were either described or would be apparent to aperson of ordinary skill. Even so, these advantages or features are notrequired in all versions of the disclosed apparatus, systems, ormethods.

Also, when reference is made in this application to a method having twoor more defined steps or operations, the defined steps or operations canbe carried out in any order or simultaneously, unless the contextexcludes those possibilities.

EXAMPLES

Illustrative examples of the disclosed technologies are provided below.An embodiment of the technologies may include one or more, and anycombination of, the examples described below.

Example 1 is a test and measurement instrument, comprising: one or moreports to connect to one or more devices under test (DUT) having tuningscrews, and to a robot; and one or more processors configured to executecode, the code to cause the one or more processors to: send commands tothe robot to position the tuning screws on the one or more DUTs to oneor more sets of positions, each set of positions comprises a parameterset for the tuning screws; acquire a set of operating parameters foreach parameter set from the one or more DUTs; generate a parameter setimage for each set of operating parameters; create a combined image ofthe parameter set images; provide the combined image to a machinelearning system to obtain a predicted set of values; adjust thepredicted set of values as needed to produce a set of predictedpositions; send commands to the robot to position the tuning screws onthe one or more DUTs to positions in the set of predicted positions;obtain a set of tuned operating parameters from the one or more DUTs;and validate operation of the one or more DUTs based upon the tunedoperating parameters.

Example 2 is the test and measurement instrument as claimed in claim 1,wherein the parameter sets comprise at least three parameter sets.

Example 3 is the test and measurement instrument of either Examples 1 or2, wherein the operating parameters comprise one of S-parameters,S-parameters in the time domain, H-parameters, Z-parameters, orX-parameters.

Example 4 is the test and measurement instrument of any of Examples 1through 3, wherein the code to cause the one or more processors to sendcommands to the robot to position the tuning screws and acquire sets ofoperating parameters, comprises code to cause the one or more processorsto: send commands to the robot to position the tuning screws to a firstset of positions corresponding to a maximum depth position for eachscrew; acquire a maximum parameter set from the DUT for the maximumdepth positions; send commands to the robot to position the tuningscrews to a second set of positions corresponding to a minimum depthposition for each screw; acquire a minimum parameter set from the DUTfor the minimum depth position; send commands to the robot to positionthe tuning screws to a third set of positions corresponding to anaverage depth position for each screw; and acquire a mean parameter setfrom the DUT for the average depth position.

Example 5 is the test and measurement instrument of any of Examples 1through 4, wherein the code to cause the one or more processors togenerate a parameter set image for each set of operating parameters,comprises code to cause the one or more processors to generate an XYplot of the operating parameters, the operating parameters being theS-parameters, and to plot an imaginary portion of the S-parameters onone axis and a real portion of the S-parameters on the other axis.

Example 6 is the test and measurement instrument of any of Examples 1through 5, wherein the code to cause the one or more processors togenerate a parameter set image comprises code to cause the one or moreprocessors to: place different operating parameters extracted from theoperating parameter sets into different quadrants of the parameter setimages, each parameter set having a corresponding parameter set image;and place the parameter set image for each operating parameter set intodifferent channels of an RGB image to produce the combined image.

Example 7 is the test and measurement instrument of any of Examples 1through 6, wherein the code to cause the one or more processors togenerate a parameter set image comprises code to cause the one or moreprocessors to: create XYZ plots of each operating parameter set, the XYZplots having the Z-axis as amplitude, the X-axis as frequency, and theY-axis is a position for each real or imaginary portion of eachS-parameter; and place XYZ plots of each operating parameter set onto adifferent channel of an RGB image.

Example 8 is the test and measurement instrument of any of Examples 1through 7, wherein the code to cause the one or more processors toadjust the predicted set of values comprises code to cause the one ormore processors to: de-normalize the predicted set of values; and addone of the parameter sets to the predicted set of values to produce thepredicted set of positions.

Example 9 is the test and measurement instrument of any of Examples 1through 8, wherein the one or more processors are further configured toexecute code to cause the one or more processors to train the machinelearning system.

Example 10 is the test and measurement instrument of Example 9, whereinthe code to cause the one or more processors to train the machinelearning system comprises code to cause the one or more processors to:tune a plurality of devices under test (DUT) having tuning screws totheir optimal tuning parameter to produce optimal tuning parameter sets;compute a mean from all the optimal tuning parameter tuning sets toobtain meanPar parameter reference sets; use the robot to position thetuning screws to positions determined by different parameter setsincluding the meanPar parameter reference set; for each parameter setacquire operational parameter sets from the plurality of DUTs; create aparameter image for each set of operational parameters; combine theparameter image of all the parameter sets into a combined image toproduce an array of combined images, the array comprising combinedimages for each of the plurality of DUTs; and use the array of combinedimages to train the machine learning system.

Example 11 is a method of testing a device under test (DUT) havingtuning elements, comprising: sending commands to a robot to positiontuning elements on the DUT to one or more sets of positions, each set ofpositions comprises a parameter set for the tuning elements; acquiring aset of operating parameters for each parameter set from the DUT;generating a parameter set image for each set of operating parameters;creating a combined image of the parameter set images; providing thecombined image to a machine learning system to obtain a predicted set ofvalues; adjusting the predicted set of values as needed to produce a setof predicted positions; sending commands to the robot to position thetuning elements on the DUT to positions in the set of predictedpositions; obtaining a set of tuned operating parameters from the DUT;and validating operation of the DUT based upon the tuned operatingparameters.

Example 12 is the method of Example 11, wherein the parameter setscomprise at least three parameter sets.

Example 13 is the method of either Examples 11 or 12, wherein theoperating parameters comprise one of S-parameters, S-parameters in thetime domain, H-parameters, Z-parameters, or X-parameters.

Example 14 is the method of any of Examples 11 through 13, wherein thetuning elements comprise tuning screws, and wherein sending commands tothe robot to position the tuning elements and acquiring sets ofoperating parameters, comprises: sending commands to the robot toposition the tuning screws to a first set of positions corresponding toa maximum depth position for each screw; acquiring a maximum parameterset from the DUT for the maximum depth positions; sending commands tothe robot to position the tuning screws to a second set of positionscorresponding to a minimum depth position for each screw; acquiring aminimum parameter set from the DUT for the minimum depth positions;sending commands to the robot to position the tuning screws to a thirdset of positions corresponding to an average depth position for eachscrew; and acquiring a mean parameter set from the DUT for the averagedepth positions.

Example 15 is the method of any of Examples 11 through 14, whereingenerating a parameter set image for each set of operating parameterscomprises generating an XY plot of the operating parameters, theoperating parameters being S-parameters, and plotting an imaginaryportion of the S-parameters on one axis and a real portion of theS-parameters on the other axis.

Example 16 is the method of any of Examples 11 through 15, whereingenerating a parameter set image comprises: placing different operatingparameters extracted from the operating parameter sets into differentquadrants of the parameter set images, each parameter set having acorresponding parameter set image; and placing the parameter set imagefor each operating parameter set into different channels of an RGB imageto produce the combined image.

Example 17 is the method of any of Examples 11 through 16, whereincreating a parameter set image comprises: creating XYZ plots of eachoperating parameter set, the XYZ plots having the Z-axis as amplitude,the X-axis as frequency, and the Y-axis is a position for each real orimaginary portion of each S-parameter; and placing XYZ plots of eachoperating parameter set onto a different channel of an RGB image.

Example 18 is the method of any of Examples 11 through 17, whereinadjusting the predicted set of values comprises: de-normalizing thepredicted set of values; and adding one of the parameter sets to thepredicted set of values to produce the predicted set of positions.

Example 19 is the method of any of Examples 11 through 18, furthercomprising training the machine learning system.

Example 20 is the method of Example 19, wherein training the machinelearning system comprises: tuning a plurality of devices under test(DUT) having tuning elements to their optimal tuning parameters toproduce optimal tuning parameter sets; computing a mean from all theoptimal tuning parameter tuning sets to obtain mean parameter referencesets; using the robot to position the tuning elements to positionsdetermined by different parameter sets, the different parameter setsincluding the mean parameter reference set; for each parameter set,acquiring operational parameter sets from the plurality of DUTs;creating a parameter image for each set of operational parameters;combining the parameter images of all the parameter sets into combinedimages to produce an array of combined images, the array comprisingcombined images for each of the plurality of DUTs; and using the arrayof combined images to train the machine learning system.

Although specific examples of the invention have been illustrated anddescribed for purposes of illustration, it will be understood thatvarious modifications may be made without departing from the spirit andscope of the invention. Accordingly, the invention should not be limitedexcept as by the appended claims.

1. A test and measurement instrument, comprising: one or more ports toconnect to one or more devices under test (DUT) having tuning screws,and to a robot; and one or more processors configured to execute code,the code to cause the one or more processors to: send commands to therobot to position the tuning screws on the one or more DUTs to one ormore sets of positions, each set of positions comprises a parameter setfor the tuning screws; acquire a set of operating parameters for eachparameter set from the one or more DUTs; generate a parameter set imagefor each set of operating parameters; create a combined image of theparameter set images; provide the combined image to a machine learningsystem to obtain a predicted set of values; adjust the predicted set ofvalues as needed to produce a set of predicted positions; send commandsto the robot to position the tuning screws on the one or more DUTs topositions in the set of predicted positions; obtain a set of tunedoperating parameters from the one or more DUTs; and validate operationof the one or more DUTs based upon the tuned operating parameters. 2.The test and measurement instrument as claimed in claim 1, wherein theparameter sets comprise at least three parameter sets.
 3. The test andmeasurement instrument as claimed in claim 1, wherein the operatingparameters comprise one of S-parameters, S-parameters in the timedomain, H-parameters, Z-parameters, or X-parameters.
 4. The test andmeasurement instrument as claimed in claim 1, wherein the code to causethe one or more processors to send commands to the robot to position thetuning screws and acquire sets of operating parameters, comprises codeto cause the one or more processors to: send commands to the robot toposition the tuning screws to a first set of positions corresponding toa maximum depth position for each screw; acquire a maximum parameter setfrom the DUT for the maximum depth positions; send commands to the robotto position the tuning screws to a second set of positions correspondingto a minimum depth position for each screw; acquire a minimum parameterset from the DUT for the minimum depth position; send commands to therobot to position the tuning screws to a third set of positionscorresponding to an average depth position for each screw; and acquire amean parameter set from the DUT for the average depth position.
 5. Thetest and measurement instrument as claimed in claim 1, wherein the codeto cause the one or more processors to generate a parameter set imagefor each set of operating parameters, comprises code to cause the one ormore processors to generate an XY plot of the operating parameters, theoperating parameters being the S-parameters, and to plot an imaginaryportion of the S-parameters on one axis and a real portion of theS-parameters on the other axis.
 6. The test and measurement instrumentas claimed in claim 1 wherein the code to cause the one or moreprocessors to generate a parameter set image comprises code to cause theone or more processors to: place different operating parametersextracted from the operating parameter sets into different quadrants ofthe parameter set images, each parameter set having a correspondingparameter set image; and place the parameter set image for eachoperating parameter set into different channels of an RGB image toproduce the combined image.
 7. The test and measurement instrument asclaimed in claim 1, wherein the code to cause the one or more processorsto generate a parameter set image comprises code to cause the one ormore processors to: create XYZ plots of each operating parameter set,the XYZ plots having the Z-axis as amplitude, the X-axis as frequency,and the Y-axis is a position for each real or imaginary portion of eachS-parameter; and place XYZ plots of each operating parameter set onto adifferent channel of an RGB image.
 8. The test and measurementinstrument as claimed in claim 1, wherein the code to cause the one ormore processors to adjust the predicted set of values comprises code tocause the one or more processors to: de-normalize the predicted set ofvalues; and add one of the parameter sets to the predicted set of valuesto produce the predicted set of positions.
 9. The test and measurementinstrument as claimed in claim 1, wherein the one or more processors arefurther configured to execute code to cause the one or more processorsto train the machine learning system.
 10. The test and measurementinstrument as claimed in claim 9, wherein the code to cause the one ormore processors to train the machine learning system comprises code tocause the one or more processors to: tune a plurality of devices undertest (DUT) having tuning screws to their optimal tuning parameter toproduce optimal tuning parameter sets; compute a mean from all theoptimal tuning parameter tuning sets to obtain meanPar parameterreference sets; use the robot to position the tuning screws to positionsdetermined by different parameter sets including the meanPar parameterreference set; for each parameter set acquire operational parameter setsfrom the plurality of DUTs; create a parameter image for each set ofoperational parameters; combine the parameter image of all the parametersets into a combined image to produce an array of combined images, thearray comprising combined images for each of the plurality of DUTs; anduse the array of combined images to train the machine learning system.11. A method of testing a device under test (DUT) having tuningelements, comprising: sending commands to a robot to position tuningelements on the DUT to one or more sets of positions, each set ofpositions comprises a parameter set for the tuning elements; acquiring aset of operating parameters for each parameter set from the DUT;generating a parameter set image for each set of operating parameters;creating a combined image of the parameter set images; providing thecombined image to a machine learning system to obtain a predicted set ofvalues; adjusting the predicted set of values as needed to produce a setof predicted positions; sending commands to the robot to position thetuning elements on the DUT to positions in the set of predictedpositions; obtaining a set of tuned operating parameters from the DUT;and validating operation of the DUT based upon the tuned operatingparameters.
 12. The method as claimed in claim 11, wherein the parametersets comprise at least three parameter sets.
 13. The method as claimedin claim 11, wherein the operating parameters comprise one ofS-parameters, S-parameters in the time domain, H-parameters,Z-parameters, or X-parameters.
 14. The method as claimed in claim 11,wherein the tuning elements comprise tuning screws, and wherein sendingcommands to the robot to position the tuning elements and acquiring setsof operating parameters, comprises: sending commands to the robot toposition the tuning screws to a first set of positions corresponding toa maximum depth position for each screw; acquiring a maximum parameterset from the DUT for the maximum depth positions; sending commands tothe robot to position the tuning screws to a second set of positionscorresponding to a minimum depth position for each screw; acquiring aminimum parameter set from the DUT for the minimum depth positions;sending commands to the robot to position the tuning screws to a thirdset of positions corresponding to an average depth position for eachscrew; and acquiring a mean parameter set from the DUT for the averagedepth positions.
 15. The method as claimed in claim 11, whereingenerating a parameter set image for each set of operating parameterscomprises generating an XY plot of the operating parameters, theoperating parameters being S-parameters, and plotting an imaginaryportion of the S-parameters on one axis and a real portion of theS-parameters on the other axis.
 16. The method as claimed in claim 11,wherein generating a parameter set image comprises: placing differentoperating parameters extracted from the operating parameter sets intodifferent quadrants of the parameter set images, each parameter sethaving a corresponding parameter set image; and placing the parameterset image for each operating parameter set into different channels of anRGB image to produce the combined image.
 17. The method as claimed inclaim 11, wherein creating a parameter set image comprises: creating XYZplots of each operating parameter set, the XYZ plots having the Z-axisas amplitude, the X-axis as frequency, and the Y-axis is a position foreach real or imaginary portion of each S-parameter; and placing XYZplots of each operating parameter set onto a different channel of an RGBimage.
 18. The method as claimed in claim 11, wherein adjusting thepredicted set of values comprises: de-normalizing the predicted set ofvalues; and adding one of the parameter sets to the predicted set ofvalues to produce the predicted set of positions.
 19. The method asclaimed in claim 11, further comprising training the machine learningsystem.
 20. The method as claimed in claim 19, wherein training themachine learning system comprises: tuning a plurality of devices undertest (DUT) having tuning elements to their optimal tuning parameters toproduce optimal tuning parameter sets; computing a mean from all theoptimal tuning parameter tuning sets to obtain mean parameter referencesets; using the robot to position the tuning elements to positionsdetermined by different parameter sets, the different parameter setsincluding the mean parameter reference set; for each parameter set,acquiring operational parameter sets from the plurality of DUTs;creating a parameter image for each set of operational parameters;combining the parameter images of all the parameter sets into combinedimages to produce an array of combined images, the array comprisingcombined images for each of the plurality of DUTs; and using the arrayof combined images to train the machine learning system.